Refactoring with C#. Safely improve .NET applications and pay down technical debt with Visual Studio, .NET 8, and C# 12

Matt Eland, Steve Smith

Software projects start as brand-new greenfield projects, but invariably become muddied in technical debt far sooner than you’d expect. In Refactoring with C#, you'll explore what technical debt is and how it arises before walking through the process of safely refactoring C# code using modern tooling in Visual Studio and more recent C# language features using C# 12 and .NET 8. This book, written by a Microsoft MVP, will guide you through the process of refactoring safely through advanced unit testing with XUnit and libraries like Moq, Snapper, and Scientist .NET. You'll explore maintainable code through SOLID principles and defensive coding techniques made possible in newer versions of C#. You'll also find out how to run code analysis and write custom Roslyn analyzers to detect and resolve issues unique to your code.The nature of coding is changing, and you'll explore how to use AI with the GitHub Copilot Chat to refactor, test, document, and generate code before ending with a discussion about communicating technical debt to leadership and getting organizational buy-in to refactor your code in enterprise organizations and in agile teams.By the end of this book, you'll understand the nature of refactoring and see how you can safely, effectively, and repeatably pay down the technical debt in your application while adding value to your business.

Python: Data Analytics and Visualization. Perform data processing and analysis with the help of python libraries, gain practical insights into predictive modeling and generate effective results in a variety of visually appealing charts using the plotting packages in Python

Martin Czygan, Phuong Vo.T.H, Ashish Kumar, Kirthi Raman

You will start the course with an introduction to the principles of data analysis and supported libraries, along with NumPy basics for statistics and data processing. Next, you will overview the Pandas package and use its powerful features to solve data-processing problems. Moving on, you will get a brief overview of the Matplotlib API .Next, you will learn to manipulate time and data structures, and load and store data in a file or database using Python packages. You will learn how to apply powerful packages in Python to process raw data into pure and helpful data using examples. You will also get a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or building helpful products such as recommendations and predictions using Scikit-learn. After this, you will move on to a data analytics specialization—predictive analytics. Social media and IOT have resulted in an avalanche of data. You will get started with predictive analytics using Python. You will see how to create predictive models from data. You will get balanced information on statistical and mathematical concepts, and implement them in Python using libraries such as Pandas, scikit-learn, and NumPy. You’ll learn more about the best predictive modeling algorithms such as Linear Regression, Decision Tree, and Logistic Regression. Finally, you will master best practices in predictive modeling.After this, you will get all the practical guidance you need to help you on the journey to effective data visualization. Starting with a chapter on data frameworks, which explains the transformation of data into information and eventually knowledge, this path subsequently cover the complete visualization process using the most popular Python libraries with working examplesThis Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:? Getting Started with Python Data Analysis, Phuong Vo.T.H &Martin Czygan•Learning Predictive Analytics with Python, Ashish Kumar•Mastering Python Data Visualization, Kirthi Raman

Game Development with Unity for .NET Developers. The ultimate guide to creating games with Unity and Microsoft Game Stack

Jiadong Chen, Ed Price

Understand what makes Unity the world’s most widely used real-time 3D development platform and explore its powerful features for creating 3D and 2D games, as well as the Unity game engine and the Microsoft Game Dev, including the Microsoft Azure Cloud and Microsoft Azure PlayFab services, to create games.You will start by getting acquainted with the Unity editor and the basic concepts of Unity script programming with C#. You'll then learn how to use C# code to work with Unity's built-in modules, such as UI, animation, physics, video, and audio, and understand how to develop a game with Unity and C#. As you progress through the chapters, you'll cover advanced topics such as the math involved in computer graphics and how to create a custom render pipeline in Unity with the new Scriptable Render Pipeline, all while optimizing performance in Unity. Along the way, you'll be introduced to Microsoft Game Dev, Azure services, and Azure PlayFab, and using the Unity3D PlayFab SDK to access the PlayFab API.By the end of this Unity book, you'll have become familiar with the Unity engine and be ready to develop your own games while also addressing the performance issues that you could encounter in the development process.

Deep Learning with TensorFlow 2 and Keras. Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API - Second Edition

Antonio Gulli, Amita Kapoor, Sujit Pal

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before.This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.

Building Apple Watch Projects. Click here to enter text

Stuart Grimshaw

With Apple’s eagerly anticipated entry into the wearable arena, the field is wide open for a new era of app development. The Apple Watch is one of the most important technologies of our time. This easy-to-understand book takes beginners on a delightful journey of discovering the features available to the developer, right up to the completion of medium-level projects ready for App Store submission. It provides the fastest way to develop real-world apps for the Apple Watch by teaching you the concepts of Watch UI, visual haptic and audio, message and data exchange between watch and phone, Web communication, and finally Visual, haptic as well as audio feedback for users.By the end of this book, you will have developed at least four fully functioning apps for deployment on watchOS 2.

Internet of Things with Python. Create exciting IoT solutions

Gaston C. Hillar

Internet of Things (IoT) is revolutionizing the way devices/things interact with each other. And when you have IoT with Python on your side, you'll be able to build interactive objects and design them. This book lets you stay at the forefront of cutting-edge research on IoT. We'll open up the possibilities using tools that enable you to interact with the world, such as Intel Galileo Gen 2, sensors, and other hardware. You will learn how to read, write, and convert digital values to generate analog output by programming Pulse Width Modulation (PWM) in Python. You will get familiar with the complex communication system included in the board, so you can interact with any shield, actuator, or sensor. Later on, you will not only see how to work with data received from the sensors, but also perform actions by sending them to a specific shield. You'll be able to connect your IoT device to the entire world, by integrating WiFi, Bluetooth, and Internet settings. With everything ready, you will see how to work in real time on your IoT device using the MQTT protocol in python.By the end of the book, you will be able to develop IoT prototypes with Python, libraries, and tools.

Hands-On Artificial Intelligence for Cybersecurity. Implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies

Alessandro Parisi

Today's organizations spend billions of dollars globally on cybersecurity. Artificial intelligence has emerged as a great solution for building smarter and safer security systems that allow you to predict and detect suspicious network activity, such as phishing or unauthorized intrusions.This cybersecurity book presents and demonstrates popular and successful AI approaches and models that you can adapt to detect potential attacks and protect your corporate systems. You'll learn about the role of machine learning and neural networks, as well as deep learning in cybersecurity, and you'll also learn how you can infuse AI capabilities into building smart defensive mechanisms. As you advance, you'll be able to apply these strategies across a variety of applications, including spam filters, network intrusion detection, botnet detection, and secure authentication.By the end of this book, you'll be ready to develop intelligent systems that can detect unusual and suspicious patterns and attacks, thereby developing strong network security defenses using AI.

Natural Language Processing with Python. Master text processing, language modeling, and NLP applications with Python's powerful tools

Cuantum Technologies LLC

Embark on a comprehensive journey to master natural language processing (NLP) with Python. Begin with foundational concepts like text preprocessing, tokenization, and key Python libraries such as NLTK, spaCy, and TextBlob. Explore the challenges of text data and gain hands-on experience in cleaning, tokenizing, and building basic NLP pipelines. Early chapters provide practical exercises to solidify your understanding of essential techniques.Advance to sophisticated topics like feature engineering using Bag of Words, TF-IDF, and embeddings like Word2Vec and BERT. Delve into language modeling with RNNs, syntax parsing, and sentiment analysis, learning to apply these techniques in real-world scenarios. Chapters on topic modeling and text summarization equip you to extract insights from data, while transformer-based models like BERT take your skills to the next level. Each concept is paired with Python-based examples, ensuring practical mastery.The final chapters focus on real-world projects, such as developing chatbots, sentiment analysis dashboards, and news aggregators. These hands-on applications challenge you to design, train, and deploy robust NLP solutions. With its structured approach and practical focus, this book equips you to confidently tackle real-world NLP challenges and innovate in the field.